Clinical ScienceDischarge decision-making after complex surgery: Surgeon behaviors compared to predictive modeling to reduce surgical readmissions

- Define the association between information used for discharge after surgery and patients' subsequent risk of readmission.
- Surgical patients were analyzed to identify criteria that statistically explained the timing of discharges.
- The same data were also used to develop a time varying prediction model of unplanned hospital readmissions.
- The models demonstrated discrepancy between what surgeons behaviourally used for discharge decision versus factors for preventing readmission.

BackgroundLittle is known about how information available at discharge affects decision-making and its effect on readmission. We sought to define the association between information used for discharge and patients' subsequent risk of readmission.Methods2009-2014 patients from a tertiary academic medical center's surgical services were analyzed using a time-to-event model to identify criteria that statistically explained the timing of discharges. The data were subsequently used to develop a time-varying prediction model of unplanned hospital readmissions. These models were validated and statistically compared.ResultsThe predictive discharge and readmission regression models were generated from a database of 20,970 patients totaling 115,976 patient-days with 1,565 readmissions (7.5%). 22 daily clinical measures were significant in both regression models. Both models demonstrated good discrimination (C statistic = 0.8 for all models). Comparison of discharge behaviors versus the predictive readmission model suggested important discordance with certain clinical measures (e.g., demographics, laboratory values) not being accounted for to optimize discharges.ConclusionsDecision-support tools for discharge may utilize variables that are not routinely considered by healthcare providers. How providers will then respond to these atypical findings may affect implementation.